{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 多尺度目标检测\n",
    "在9.4节（锚框）中，我们在实验中以输入图像的每个像素为中心生成多个锚框。这些锚框是对输入图像不同区域的采样。然而，如果以图像每个像素为中心都生成锚框，很容易生成过多锚框而造成计算量过大。举个例子，假设输入图像的高和宽分别为561像素和728像素，如果以每个像素为中心生成5个不同形状的锚框，那么一张图像上则需要标注并预测200多万个锚框（561×728×5）。\n",
    "\n",
    "减少锚框个数并不难。一种简单的方法是在输入图像中均匀采样一小部分像素，并以采样的像素为中心生成锚框。此外，在不同尺度下，我们可以生成不同数量和不同大小的锚框。值得注意的是，较小目标比较大目标在图像上出现位置的可能性更多。举个简单的例子：形状为1×1、1×2和2×2的目标在形状为2×2的图像上可能出现的位置分别有4、2和1种。因此，当使用较小锚框来检测较小目标时，我们可以采样较多的区域；而当使用较大锚框来检测较大目标时，我们可以采样较少的区域。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "为了演示如何多尺度生成锚框，我们先读取一张图像。它的高和宽分别为561像素和728像素。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "from PIL import Image\n",
    "from matplotlib import pyplot as plt\n",
    "from IPython import display\n",
    "import numpy as np\n",
    "import torch\n",
    "import math\n",
    "import sys\n",
    "\n",
    "def use_svg_display():\n",
    "    display.set_matplotlib_formats('svg')\n",
    "\n",
    "def set_figsize(figsize=(3.5, 2.5)):\n",
    "    use_svg_display()\n",
    "    plt.rcParams['figure.figsize'] = figsize\n",
    "    \n",
    "def MultiBoxPrior(feature_map, sizes=[0.75, 0.5, 0.25], ratios=[1, 2, 0.5]):\n",
    "    \"\"\"\n",
    "    anchor表示成(xmin, ymin, xmax, ymax)\n",
    "    Args:\n",
    "        feature_map: torch tensor, Shape: [N, C, H, W].\n",
    "        sizes: List of sizes (0~1) of generated MultiBoxPriores. \n",
    "        ratios: List of aspect ratios (non-negative) of generated MultiBoxPriores. \n",
    "    Returns:\n",
    "        anchors of shape (1, num_anchors, 4). 由于batch里每个都一样, 所以第一维为1\n",
    "    \"\"\"\n",
    "    pairs = [] # pair for (size, sqrt(ration))\n",
    "    for r in ratios:\n",
    "        # (s1,r1),(s1,r2),…,(s1,rm)\n",
    "        pairs.append([sizes[0], math.sqrt(r)])\n",
    "    for s in sizes[1: ]:\n",
    "        # (s2,r1),(s3,r1),…,(sn,r1)\n",
    "        pairs.append([s, math.sqrt(ratios[0])])\n",
    "    pairs = np.array(pairs)\n",
    "    ss1 = pairs[:, 0] * pairs[:, 1] # size * sqrt(ration)\n",
    "    ss2 = pairs[:, 0] / pairs[:, 1] # size / sqrt(ration)\n",
    "    base_anchors = np.stack([-ss1, -ss2, ss1, ss2], axis=1)/2\n",
    "    \n",
    "    h, w = feature_map.shape[-2:]\n",
    "    shifts_x = np.arange(0, w) / w\n",
    "    shifts_y = np.arange(0, h) / h\n",
    "    shift_x, shift_y = np.meshgrid(shifts_x, shifts_y)\n",
    "    shift_x = shift_x.reshape(-1)\n",
    "    shift_y = shift_y.reshape(-1)\n",
    "    shifts = np.stack((shift_x, shift_y, shift_x, shift_y), axis=1)\n",
    "    \n",
    "    # 这个地方应该是用到了广播机制\n",
    "    anchors = shifts.reshape((-1, 1, 4)) + base_anchors.reshape((1, -1, 4)) \n",
    "    \n",
    "    return torch.tensor(anchors, dtype=torch.float32).view(1, -1, 4)\n",
    "\n",
    "def bbox_to_rect(bbox, color):\n",
    "    # 将边界框(左上x, 左上y, 右下x, 右下y)格式转换成matplotlib格式：((左上x, 左上y), 宽, 高)\n",
    "    return plt.Rectangle(xy=(bbox[0],bbox[1]), width=bbox[2]-bbox[0], height=bbox[3]-bbox[1], fill=False, edgecolor=color, linewidth=2)\n",
    "\n",
    "\n",
    "def show_bboxes(axes, bboxes, labels=None, colors=None):\n",
    "    # bboxes = [[x,y,x,y], [x,y,x,y],...,[x,y,x,y]]\n",
    "    def _make_list(obj, default_values=None):\n",
    "        if obj is None:\n",
    "            obj = default_values\n",
    "        elif not isinstance(obj, (list, tuple)):\n",
    "            obj = [obj]\n",
    "        return obj\n",
    "    \n",
    "    labels = _make_list(labels)\n",
    "    colors = _make_list(colors, ['b', 'g', 'r', 'm', 'c'])\n",
    "    for i, bbox in enumerate(bboxes):\n",
    "        color = colors[i % len(colors)]\n",
    "        rect = bbox_to_rect(bbox.detach().cpu().numpy(), color)\n",
    "        axes.add_patch(rect)\n",
    "        if labels and len(labels) > i:\n",
    "            text_color = 'k' if color == 'w' else 'w' # 用白色显示文字，但是如果框的颜色color已经是白色，则文字用k(接近黑色)\n",
    "            axes.text(rect.xy[0], rect.xy[1], labels[i], va='center', ha='center', fontsize=5, color=text_color, bbox=dict(facecolor=color,lw=0))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(728, 561)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img = Image.open(\"Datasets/catdog.jpg\")\n",
    "w, h = img.size\n",
    "w, h"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我们在5.1节（二维卷积层）中将卷积神经网络的二维数组输出称为特征图。 我们可以通过定义特征图的形状来确定任一图像上均匀采样的锚框中心。\n",
    "\n",
    "下面定义display_anchors函数。我们在特征图fmap上以每个单元（像素）为中心生成锚框anchors。由于锚框anchors中x和y轴的坐标值分别已除以特征图fmap的宽和高，这些值域在0和1之间的值表达了锚框在特征图中的相对位置。由于锚框anchors的中心遍布特征图fmap上的所有单元，anchors的中心在任一图像的空间相对位置一定是均匀分布的。具体来说，当特征图的宽和高分别设为fmap_w和fmap_h时，该函数将在任一图像上均匀采样fmap_h行fmap_w列个像素，并分别以它们为中心生成大小为s（假设列表s长度为1）的不同宽高比（ratios）的锚框。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "set_figsize()\n",
    "\n",
    "def display_anchors(fmap_w, fmap_h, s):\n",
    "    # 前两维的取值1,10不影响输出结果\n",
    "    fmap = torch.zeros((1, 10, fmap_h, fmap_w), dtype=torch.float32)\n",
    "    \n",
    "    # 平移所有锚框使均匀分布在图片上\n",
    "    offset_x, offset_y = 1.0 / fmap_w, 1.0 / fmap_h\n",
    "    anchors = MultiBoxPrior(fmap, sizes=s, ratios=[1, 2, 0.5]) + torch.tensor([offset_x/2, offset_y/2, offset_x/2, offset_y/2])\n",
    "    bbox_scale = torch.tensor([[w, h, w, h]], dtype=torch.float32)\n",
    "    show_bboxes(plt.imshow(img).axes, anchors[0]*bbox_scale)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我们先关注小目标的检测。为了在显示时更容易分辨，这里令不同中心的锚框不重合：设锚框大小为0.15，特征图的高和宽分别为2和4。可以看出，图像上2行4列的锚框中心分布均匀。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "  <g id=\"axes_1\">\n",
       "   <g id=\"patch_2\">\n",
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    "将特征图的高和宽分别减半，并用更大的锚框检测更大的目标。当锚框大小设0.4时，有些锚框的区域有重合"
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   "metadata": {},
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    "最后，我们将特征图的宽进一步减半至1，并将锚框大小增至0.8。此时锚框中心即图像中心。"
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   "metadata": {},
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    "既然我们已在多个尺度上生成了不同大小的锚框，相应地，我们需要在不同尺度下检测不同大小的目标。下面我们来介绍一种基于卷积神经网络的方法。\n",
    "\n",
    "在某个尺度下，假设我们依据ci张形状为h×w的特征图生成h×w组不同中心的锚框，且每组的锚框个数为aa。例如，在刚才实验的第一个尺度下，我们依据10（通道数）张形状为4×2的特征图生成了8组不同中心的锚框，且每组含3个锚框。 接下来，依据真实边界框的类别和置，每个锚框将被标注类别和偏移量。在当前的尺度下，目标检测模型需要根据输入图像预测h×w组不同中心的锚框的类别和偏移量。\n",
    "\n",
    "假设这里的ci\t张特征图为卷积神经网络根据输入图像做前向计算所得的中间输出。既然每张特征图上都有h×wh×w个不同的空间位置，那么相同空间位置可以看作含有ci个单元。 根据5.1节（二维卷积层）中感受野的定义，特征图在相同空间位置的ci\t个单元在输入图像上的感受野相同，并表征了同一感受野内的输入图像信息。 因此，我们可以将特征图在相同空间位置的ci个单元变换为以该位置为中心生成的aa个锚框的类别和偏移量。 不难发现，本质上，我们用输入图像在某个感受野区域内的信息来预测输入图像上与该区域位置相近的锚框的类别和偏移量。\n",
    "\n",
    "当不同层的特征图在输入图像上分别拥有不同大小的感受野时，它们将分别用来检测不同大小的目标。例如，我们可以通过设计网络，令较接近输出层的特征图中每个单元拥有更广阔的感受野，从而检测输入图像中更大尺寸的目标。"
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   "source": [
    "### 小结\n",
    "1、可以在多个尺度下生成不同数量和不同大小的锚框，从而在多个尺度下检测不同大小的目标。\n",
    "\n",
    "2、特征图的形状能确定任一图像上均匀采样的锚框中心\n",
    "\n",
    "3、用输入图像在某个感受野区域的信息来预测输入图像与该区域相近的锚框的类别和偏移量。"
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